{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/davidbarker/Google Drive/Research/Facts and Premises/Premises BJPOLS Dataverse Study 2 2013 data 7 2020.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 8 Jul 2020, 07:08:24

{com}. do "/Users/davidbarker/Google Drive/Research/Facts and Premises/Premises BJPOLS Dataverse Study 2 2013 data 7 2020.do"
{txt}
{com}. * stata version 15
. 
. 
.  * now for Study II: 2013 analysis
. 
. use "/Users/davidbarker/Google Drive/Research/Facts and Premises/Premises 13  Harvard Dataverse 7 2020.dta"
{txt}
{com}. 
. * making the issue ideology factor
. 
. factor debtcripfalse01 crimesocialnotpunish supportACA affirmactsupport envirooverjobs abortprochoice immigrantlegal gunbanassrifles afghanmistake iraqmistake supportgaymarriage, ml factors (1)
{txt}(obs=941)
Iteration 0:   log likelihood = {res}-308.16046
{txt}Iteration 1:   log likelihood = {res}-268.95341
{txt}Iteration 2:   log likelihood = {res}-268.44551
{txt}Iteration 3:   log likelihood = {res} -268.4373
{txt}Iteration 4:   log likelihood = {res}-268.43711

{txt}Factor analysis/correlation{col 50}Number of obs    = {res}       941
{col 5}{txt}Method: maximum likelihood{col 50}Retained factors =   {res}       1
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      11
{col 50}{txt}Schwarz's BIC    =   {res} 612.191
{col 5}{txt}Log likelihood = {res}-268.4371{col 50}{txt}(Akaike's) AIC   =   {res} 558.874

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      3.09751            .            1.0000       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}55{txt}) ={res} 2426.25{txt} Prob>chi2 ={res} 0.0000
{txt}{col 5}LR test: {res}   1{txt} factor vs. saturated:  chi2({res}44{txt}) ={res}  533.93{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:debtcripf~01}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5866}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6558}}}{space 1}
{space 4}{space 0}{ralign 12:crimesocia~h}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5705}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6745}}}{space 1}
{space 4}{space 0}{ralign 12:supportACA}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6888}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5255}}}{space 1}
{space 4}{space 0}{ralign 12:affirmacts~t}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5869}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6555}}}{space 1}
{space 4}{space 0}{ralign 12:enviroover~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4930}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7569}}}{space 1}
{space 4}{space 0}{ralign 12:abortproch~e}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4672}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7817}}}{space 1}
{space 4}{space 0}{ralign 12:immigrantl~l}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4751}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7743}}}{space 1}
{space 4}{space 0}{ralign 12:gunbanassr~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4824}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7673}}}{space 1}
{space 4}{space 0}{ralign 12:afghanmist~e}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2588}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9330}}}{space 1}
{space 4}{space 0}{ralign 12:iraqmistake}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5097}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7402}}}{space 1}
{space 4}{space 0}{ralign 12:supportgay~e}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6019}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6377}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. predict ideolissuefactor1101
{txt}(regression scoring assumed)

{p 0 0 2}Scoring coefficients (method = regression){p_end}

{space 4}{hline 13}{c  TT}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}
{space 4}{hline 13}{c   +}{hline 10}
{space 4}{space 0}{ralign 12:debtcripf~01}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.15909}}}{space 1}
{space 4}{space 0}{ralign 12:crimesocia~h}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.15047}}}{space 1}
{space 4}{space 0}{ralign 12:supportACA}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.23310}}}{space 1}
{space 4}{space 0}{ralign 12:affirmacts~t}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.15925}}}{space 1}
{space 4}{space 0}{ralign 12:enviroover~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.11586}}}{space 1}
{space 4}{space 0}{ralign 12:abortproch~e}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.10632}}}{space 1}
{space 4}{space 0}{ralign 12:immigrantl~l}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.10914}}}{space 1}
{space 4}{space 0}{ralign 12:gunbanassr~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.11184}}}{space 1}
{space 4}{space 0}{ralign 12:afghanmist~e}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.04934}}}{space 1}
{space 4}{space 0}{ralign 12:iraqmistake}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.12251}}}{space 1}
{space 4}{space 0}{ralign 12:supportgay~e}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.16794}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}


{com}. sum ideolissuefactor1101

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
ideolis~1101 {c |}{res}        941    1.88e-10    .9067052  -1.906932   1.901229
{txt}
{com}. replace ideolissuefactor1101 = ideolissuefactor1101+  1.906932
{txt}(941 real changes made)

{com}. sum ideolissuefactor1101

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
ideolis~1101 {c |}{res}        941    1.906932    .9067052   2.42e-07   3.808161
{txt}
{com}. replace ideolissuefactor1101 = ideolissuefactor1101 / 3.808161
{txt}(941 real changes made)

{com}. 
. * making the humannature selfish and competitive measure, which is a summed index of two survey items
. gen premhumanselfishcompete201 = (prem_peoplecoopvscompete01+ prem_pplseekowninterest01) /2
{txt}(22 missing values generated)

{com}. 
. * making the progress skepticism factor
. factor UML15noskip UML14noskip UML13noskip UML12noskip, ml factors (1)
{txt}(obs=964)
Iteration 0:   log likelihood = {res}-20.040096
{txt}Iteration 1:   log likelihood = {res}-.13458091
{txt}Iteration 2:   log likelihood = {res}-.08990815
{txt}Iteration 3:   log likelihood = {res}-.08988986

{txt}Factor analysis/correlation{col 50}Number of obs    = {res}       964
{col 5}{txt}Method: maximum likelihood{col 50}Retained factors =   {res}       1
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}       4
{col 50}{txt}Schwarz's BIC    =   {res} 27.6641
{col 5}{txt}Log likelihood = {res}-.0898899{col 50}{txt}(Akaike's) AIC   =   {res} 8.17978

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.03551            .            1.0000       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res}  300.79{txt} Prob>chi2 ={res} 0.0000
{txt}{col 5}LR test: {res}   1{txt} factor vs. saturated:  chi2({res}2{txt})  ={res}    0.18{txt} Prob>chi2 ={res} 0.9143

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:UML15noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4311}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8141}}}{space 1}
{space 4}{space 0}{ralign 12:UML14noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4181}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8252}}}{space 1}
{space 4}{space 0}{ralign 12:UML13noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.4544}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7936}}}{space 1}
{space 4}{space 0}{ralign 12:UML12noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6844}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5316}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. predict prem_progskepticalfactor
{txt}(regression scoring assumed)

{p 0 0 2}Scoring coefficients (method = regression){p_end}

{space 4}{hline 13}{c  TT}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}
{space 4}{hline 13}{c   +}{hline 10}
{space 4}{space 0}{ralign 12:UML15noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.20515}}}{space 1}
{space 4}{space 0}{ralign 12:UML14noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.19631}}}{space 1}
{space 4}{space 0}{ralign 12:UML13noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.22180}}}{space 1}
{space 4}{space 0}{ralign 12:UML12noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.49870}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}


{com}. gen prem_progskeptfactor01 = prem_progskepticalfactor + 1.260103
{txt}(36 missing values generated)

{com}. replace prem_progskeptfactor01 = prem_progskeptfactor01 /3.701029
{txt}(964 real changes made)

{com}. sum prem_progskeptfactor01

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
prem_prog~01 {c |}{res}        964    .3404737    .2114787  -6.10e-08   .9999999
{txt}
{com}. 
. *making the societyfragile factor
. 
. factor UML07noskip UML08noskip UML09noskip UML10noskip UML11noskip, ml factors (1)
{txt}(obs=955)
Iteration 0:   log likelihood = {res}-36.429502
{txt}Iteration 1:   log likelihood = {res}-16.304603
{txt}Iteration 2:   log likelihood = {res}-16.237895
{txt}Iteration 3:   log likelihood = {res}-16.236133
{txt}Iteration 4:   log likelihood = {res}-16.236085

{txt}Factor analysis/correlation{col 50}Number of obs    = {res}       955
{col 5}{txt}Method: maximum likelihood{col 50}Retained factors =   {res}       1
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}       5
{col 50}{txt}Schwarz's BIC    =   {res} 66.7807
{col 5}{txt}Log likelihood = {res}-16.23609{col 50}{txt}(Akaike's) AIC   =   {res} 42.4722

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.64242            .            1.0000       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}10{txt}) ={res}  756.72{txt} Prob>chi2 ={res} 0.0000
{txt}{col 5}LR test: {res}   1{txt} factor vs. saturated:  chi2({res}5{txt})  ={res}   32.36{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:UML07noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6923}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5207}}}{space 1}
{space 4}{space 0}{ralign 12:UML08noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.4439}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8029}}}{space 1}
{space 4}{space 0}{ralign 12:UML09noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5754}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6690}}}{space 1}
{space 4}{space 0}{ralign 12:UML10noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5216}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7280}}}{space 1}
{space 4}{space 0}{ralign 12:UML11noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6025}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6370}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. predict prem_socfragilefactor01
{txt}(regression scoring assumed)

{p 0 0 2}Scoring coefficients (method = regression){p_end}

{space 4}{hline 13}{c  TT}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}
{space 4}{hline 13}{c   +}{hline 10}
{space 4}{space 0}{ralign 12:UML07noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.36886}}}{space 1}
{space 4}{space 0}{ralign 12:UML08noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.15340}}}{space 1}
{space 4}{space 0}{ralign 12:UML09noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.23866}}}{space 1}
{space 4}{space 0}{ralign 12:UML10noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.19877}}}{space 1}
{space 4}{space 0}{ralign 12:UML11noskip}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.26240}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}


{com}. sum prem_socfragilefactor01

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
prem_socf~01 {c |}{res}        955    2.24e-10    .8500256    -1.2696   2.566818
{txt}
{com}. replace prem_socfragilefactor01 = prem_socfragilefactor01 + 1.2696
{txt}(955 real changes made)

{com}. sum prem_socfragilefactor01

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
prem_socf~01 {c |}{res}        955      1.2696    .8500256  -2.72e-07   3.836418
{txt}
{com}. replace prem_socfragilefactor01 = prem_socfragilefactor01 / 2.566818
{txt}(955 real changes made)

{com}. replace prem_socfragilefactor01 = prem_socfragilefactor01*-1
{txt}(955 real changes made)

{com}. sum prem_socfragilefactor01

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
prem_socf~01 {c |}{res}        955   -.4946202    .3311593   -1.49462   1.06e-07
{txt}
{com}. replace prem_socfragilefactor01 = prem_socfragilefactor01 + 1.49462
{txt}(955 real changes made)

{com}. 
. * running the regressions: first, using the three-point ideological identification outcome variable
. gologit2 ideo301 commun_indiv01 pacif_militarism01 newways_trad01 premhumanselfishcompete201 prem_progskeptfactor01 prem_socfragilefactor01 female white educ01 age01 famincnom01, or cluster (cdid113)
{res}
{txt}Generalized Ordered Logit Estimates{col 49}Number of obs{col 67}= {res}       830
{txt}{col 49}Wald chi2({res}22{txt}){col 67}= {res}   1279.31
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-694.15637{txt}{col 49}Pseudo R2{col 67}= {res}    0.2343

{txt}{ralign 92:(Std. Err. adjusted for {res:48} clusters in cdid113)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                   ideo301{col 28}{c |} Odds Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}0                          {txt}{c |}
{space 12}commun_indiv01 {c |}{col 28}{res}{space 2} 3.059894{col 40}{space 2} .7391203{col 51}{space 1}    4.63{col 60}{space 3}0.000{col 68}{space 4}  1.90589{col 81}{space 3}  4.91264
{txt}{space 8}pacif_militarism01 {c |}{col 28}{res}{space 2} 2.945102{col 40}{space 2} .7291173{col 51}{space 1}    4.36{col 60}{space 3}0.000{col 68}{space 4} 1.812879{col 81}{space 3} 4.784448
{txt}{space 12}newways_trad01 {c |}{col 28}{res}{space 2} 9.773746{col 40}{space 2} 2.278469{col 51}{space 1}    9.78{col 60}{space 3}0.000{col 68}{space 4} 6.189104{col 81}{space 3} 15.43456
{txt}premhumanselfishcompete201 {c |}{col 28}{res}{space 2}  1.75456{col 40}{space 2} .6418478{col 51}{space 1}    1.54{col 60}{space 3}0.124{col 68}{space 4} .8566121{col 81}{space 3} 3.593786
{txt}{space 4}prem_progskeptfactor01 {c |}{col 28}{res}{space 2} 2.144886{col 40}{space 2} .9483224{col 51}{space 1}    1.73{col 60}{space 3}0.084{col 68}{space 4} .9016978{col 81}{space 3} 5.102083
{txt}{space 3}prem_socfragilefactor01 {c |}{col 28}{res}{space 2}  2.80023{col 40}{space 2} .8761337{col 51}{space 1}    3.29{col 60}{space 3}0.001{col 68}{space 4} 1.516598{col 81}{space 3} 5.170314
{txt}{space 20}female {c |}{col 28}{res}{space 2} .8137114{col 40}{space 2}  .128141{col 51}{space 1}   -1.31{col 60}{space 3}0.191{col 68}{space 4} .5976206{col 81}{space 3} 1.107937
{txt}{space 21}white {c |}{col 28}{res}{space 2} 1.533256{col 40}{space 2} .2443751{col 51}{space 1}    2.68{col 60}{space 3}0.007{col 68}{space 4} 1.121883{col 81}{space 3} 2.095471
{txt}{space 20}educ01 {c |}{col 28}{res}{space 2} .7338178{col 40}{space 2} .1591452{col 51}{space 1}   -1.43{col 60}{space 3}0.154{col 68}{space 4} .4797176{col 81}{space 3} 1.122512
{txt}{space 21}age01 {c |}{col 28}{res}{space 2} 3.488886{col 40}{space 2}  1.76372{col 51}{space 1}    2.47{col 60}{space 3}0.013{col 68}{space 4} 1.295337{col 81}{space 3} 9.397032
{txt}{space 15}famincnom01 {c |}{col 28}{res}{space 2} 1.603869{col 40}{space 2} .5817328{col 51}{space 1}    1.30{col 60}{space 3}0.193{col 68}{space 4} .7878312{col 81}{space 3} 3.265162
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .0433168{col 40}{space 2} .0179368{col 51}{space 1}   -7.58{col 60}{space 3}0.000{col 68}{space 4} .0192392{col 81}{space 3}  .097527
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_5                         {txt}{c |}
{space 12}commun_indiv01 {c |}{col 28}{res}{space 2}  2.92039{col 40}{space 2} .8859106{col 51}{space 1}    3.53{col 60}{space 3}0.000{col 68}{space 4} 1.611484{col 81}{space 3} 5.292439
{txt}{space 8}pacif_militarism01 {c |}{col 28}{res}{space 2} 1.605498{col 40}{space 2} .2583469{col 51}{space 1}    2.94{col 60}{space 3}0.003{col 68}{space 4} 1.171223{col 81}{space 3} 2.200796
{txt}{space 12}newways_trad01 {c |}{col 28}{res}{space 2} 10.57595{col 40}{space 2} 2.462081{col 51}{space 1}   10.13{col 60}{space 3}0.000{col 68}{space 4} 6.701306{col 81}{space 3} 16.69087
{txt}premhumanselfishcompete201 {c |}{col 28}{res}{space 2} 1.099494{col 40}{space 2} .5934684{col 51}{space 1}    0.18{col 60}{space 3}0.861{col 68}{space 4} .3817191{col 81}{space 3} 3.166954
{txt}{space 4}prem_progskeptfactor01 {c |}{col 28}{res}{space 2} 3.747586{col 40}{space 2} 1.479189{col 51}{space 1}    3.35{col 60}{space 3}0.001{col 68}{space 4} 1.728934{col 81}{space 3} 8.123154
{txt}{space 3}prem_socfragilefactor01 {c |}{col 28}{res}{space 2} 3.157474{col 40}{space 2} .8238608{col 51}{space 1}    4.41{col 60}{space 3}0.000{col 68}{space 4} 1.893393{col 81}{space 3} 5.265489
{txt}{space 20}female {c |}{col 28}{res}{space 2} .9902519{col 40}{space 2} .1711434{col 51}{space 1}   -0.06{col 60}{space 3}0.955{col 68}{space 4} .7057228{col 81}{space 3} 1.389496
{txt}{space 21}white {c |}{col 28}{res}{space 2} 1.545505{col 40}{space 2} .3263387{col 51}{space 1}    2.06{col 60}{space 3}0.039{col 68}{space 4}  1.02173{col 81}{space 3} 2.337785
{txt}{space 20}educ01 {c |}{col 28}{res}{space 2}  .807168{col 40}{space 2} .2025496{col 51}{space 1}   -0.85{col 60}{space 3}0.393{col 68}{space 4}  .493588{col 81}{space 3} 1.319968
{txt}{space 21}age01 {c |}{col 28}{res}{space 2} .6707594{col 40}{space 2} .2593886{col 51}{space 1}   -1.03{col 60}{space 3}0.302{col 68}{space 4}   .31434{col 81}{space 3} 1.431311
{txt}{space 15}famincnom01 {c |}{col 28}{res}{space 2} .5283308{col 40}{space 2} .2372728{col 51}{space 1}   -1.42{col 60}{space 3}0.155{col 68}{space 4} .2190948{col 81}{space 3}  1.27403
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .0119096{col 40}{space 2}  .005865{col 51}{space 1}   -9.00{col 60}{space 3}0.000{col 68}{space 4} .0045364{col 81}{space 3} .0312666
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}

{com}. 
. * next, the 11 point policy ideology factor outcome variable
. reg ideolissuefactor1101 commun_indiv01 pacif_militarism01 newways_trad01 premhumanselfishcompete201 prem_progskeptfactor01 prem_socfragilefactor01 female white educ01 age01 famincnom01, cluster (cdid113)

{txt}Linear regression                               Number of obs     = {res}       858
                                                {txt}F(11, 47)         =  {res}   796.64
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.6377
                                                {txt}Root MSE          =    {res} .14562

{txt}{ralign 92:(Std. Err. adjusted for {res:48} clusters in cdid113)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}      ideolissuefactor1101{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}commun_indiv01 {c |}{col 28}{res}{space 2}-.1197036{col 40}{space 2} .0128076{col 51}{space 1}   -9.35{col 60}{space 3}0.000{col 68}{space 4}-.1454691{col 81}{space 3}-.0939381
{txt}{space 8}pacif_militarism01 {c |}{col 28}{res}{space 2}-.1001748{col 40}{space 2} .0164114{col 51}{space 1}   -6.10{col 60}{space 3}0.000{col 68}{space 4}-.1331902{col 81}{space 3}-.0671594
{txt}{space 12}newways_trad01 {c |}{col 28}{res}{space 2}-.2558365{col 40}{space 2} .0105108{col 51}{space 1}  -24.34{col 60}{space 3}0.000{col 68}{space 4}-.2769816{col 81}{space 3}-.2346915
{txt}premhumanselfishcompete201 {c |}{col 28}{res}{space 2}  .033956{col 40}{space 2}  .018787{col 51}{space 1}    1.81{col 60}{space 3}0.077{col 68}{space 4}-.0038385{col 81}{space 3} .0717505
{txt}{space 4}prem_progskeptfactor01 {c |}{col 28}{res}{space 2}-.1772389{col 40}{space 2} .0252812{col 51}{space 1}   -7.01{col 60}{space 3}0.000{col 68}{space 4}-.2280981{col 81}{space 3}-.1263797
{txt}{space 3}prem_socfragilefactor01 {c |}{col 28}{res}{space 2}-.1775792{col 40}{space 2} .0207803{col 51}{space 1}   -8.55{col 60}{space 3}0.000{col 68}{space 4}-.2193837{col 81}{space 3}-.1357747
{txt}{space 20}female {c |}{col 28}{res}{space 2}  .026613{col 40}{space 2} .0113704{col 51}{space 1}    2.34{col 60}{space 3}0.024{col 68}{space 4} .0037387{col 81}{space 3} .0494874
{txt}{space 21}white {c |}{col 28}{res}{space 2}-.0320853{col 40}{space 2} .0171293{col 51}{space 1}   -1.87{col 60}{space 3}0.067{col 68}{space 4}-.0665451{col 81}{space 3} .0023744
{txt}{space 20}educ01 {c |}{col 28}{res}{space 2} .0345077{col 40}{space 2} .0167184{col 51}{space 1}    2.06{col 60}{space 3}0.045{col 68}{space 4} .0008747{col 81}{space 3} .0681408
{txt}{space 21}age01 {c |}{col 28}{res}{space 2} .0725295{col 40}{space 2} .0203375{col 51}{space 1}    3.57{col 60}{space 3}0.001{col 68}{space 4} .0316158{col 81}{space 3} .1134433
{txt}{space 15}famincnom01 {c |}{col 28}{res}{space 2} .0403054{col 40}{space 2} .0332562{col 51}{space 1}    1.21{col 60}{space 3}0.232{col 68}{space 4}-.0265974{col 81}{space 3} .1072081
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .9129816{col 40}{space 2} .0299233{col 51}{space 1}   30.51{col 60}{space 3}0.000{col 68}{space 4} .8527837{col 81}{space 3} .9731796
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
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